Solar Wind Speed Estimate with Machine Learning Ensemble Models for LISA

TL;DR

This paper demonstrates that ensemble machine learning models can accurately estimate solar wind speed using cosmic-ray flux data, potentially replacing dedicated instruments in space missions like LISA.

Contribution

The study introduces ensemble machine learning models that outperform individual regressors in reconstructing solar wind speed from cosmic-ray data, offering a software-based diagnostic alternative.

Findings

Ensemble models outperform individual regressors in accuracy.

Machine learning models can substitute dedicated space instrumentation.

Potential for improved space weather forecasting and mission diagnostics.

Abstract

In this work we study the potentialities of machine learning models in reconstructing the solar wind speed observations gathered in the first Lagrangian point by the ACE satellite in 2016--2017 using as input data galactic cosmic-ray flux variations measured with particle detectors hosted onboard the LISA Pathfinder mission also orbiting around L1 during the same years. We show that ensemble models composed of heterogeneous weak regressors are able to outperform weak regressors in terms of predictive accuracy. Machine learning and other powerful predictive algorithms open a window on the possibility of substituting dedicated instrumentation with software models acting as surrogates for diagnostics of space missions such as LISA and space weather science.